Qwen 2.5 Coder 7B local setup guide.
Efficient coding model for local code assistance on consumer hardware. Architecture: dense transformer. Best for: local coding assistant; repo Q&A on developer laptops; Mac unified memory setup. Avoid if: you need strongest autonomous agentic coding quality; you only have 4GB RAM. Cloud fallback: Use cloud models for long autonomous coding runs or very large repository context. Hardware requirements start at 8GB RAM and 6GB VRAM, with 12GB RAM and 8GB VRAM recommended. Quant recommendations include Q4_K_M on Ollama, Q8_0 on Ollama, GGUF Q4_K_M on LM Studio, GGUF Q4_K_M on llama.cpp, MLX 4-bit on MLX. Runtime notes: Ollama: Works on macOS, Windows, and Linux; GPU acceleration depends on local driver support. Ollama: Works on macOS, Windows, and Linux; GPU acceleration depends on local driver support. LM Studio: Best for desktop macOS, Windows, and Linux users who want a GUI runtime. llama.cpp: Works across macOS, Windows, and Linux; command flags may vary by build. MLX: Apple Silicon macOS only; this path assumes unified memory.. Setup commands: Ollama: ollama pull qwen2.5-coder:7b Ollama: ollama pull qwen2.5-coder:7b LM Studio: Open LM Studio, search Qwen2.5-Coder-7B-Instruct-GGUF, download Q4_K_M, then start local server. llama.cpp: llama-cli -hf bartowski/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M -p "Write a coding plan" MLX: python -m mlx_lm.generate --model mlx-community/Qwen2.5-Coder-7B-Instruct-4bit --prompt "Write a coding plan". Check this model on my machine at /calculator?task=coding_assistant&runtime=ollama&os=macos&ramGb=16&gpuTier=mid&unifiedMemory=1&model=qwen2.5-coder%3A7b, Save model profile, or Generate free model report after login.